NDLI: Learning Sparse Representations of Depth

Content Provider	IEEE Xplore Digital Library
Author	Tosic, I. Olshausen, B.A. Culpepper, B.J.
Copyright Year	2007
Abstract	This paper introduces a new method for learning and inferring sparse representations of depth (disparity) maps. The proposed algorithm relaxes the usual assumption of the stationary noise model in sparse coding. This enables learning from data corrupted with spatially varying noise or uncertainty, such as that obtained by laser range scanners or structured light depth cameras. Sparse representations are learned from the Middlebury database disparity maps and then exploited in a two-layer graphical model for inferring depth from stereo, by including a sparsity prior on the learned features. Since they capture higher order dependencies in the depth structure, these priors can complement smoothness priors commonly used in depth inference based on Markov random field (MRF) models. Inference on the proposed graph is achieved using an alternating iterative optimization technique, where the first layer is solved using an existing MRF-based stereo matching algorithm, then held fixed as the second layer is solved using the proposed nonstationary sparse coding algorithm. This leads to a general method for improving solutions of state-of-the-art MRF-based depth estimation algorithms. Our experimental results first show that depth inference using learned representations leads to state-of-the-art denoising of depth maps obtained from laser range scanners and a time of flight camera. Furthermore, we show that adding sparse priors improves the results of depth estimation methods based on graph cut optimization of MRFs with first and second order priors.
Sponsorship	IEEE Signal Processing Society
Starting Page	941
Ending Page	952
Page Count	12
File Size	3380795
File Format	PDF
ISSN	19324553
Volume Number	5
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise Dictionaries Encoding Inference algorithms Stereo vision Noise reduction Optimization sparse approximations Depth denoising depth from stereo dictionary learning
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A Proximal Method for Dictionary Updating in Sparse Representations

Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation

Online Sparsifying Transform Learning— Part I: Algorithms

Improving Dictionary Learning: Multiple Dictionary Updates and Coefficient Reuse

Fast Dictionary Learning for Sparse Representations of Speech Signals

Simultaneous Codeword Optimization (SimCO) for Dictionary Update and Learning

Learning Ancestral Atom via Sparse Coding

Online Sparsifying Transform Learning—Part II: Convergence Analysis

Correlation Based Online Dictionary Learning Algorithm

Learning Sparse Representations of Depth

Similar Documents

A Proximal Method for Dictionary Updating in Sparse Representations

Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation

Online Sparsifying Transform Learning— Part I: Algorithms

Improving Dictionary Learning: Multiple Dictionary Updates and Coefficient Reuse

Fast Dictionary Learning for Sparse Representations of Speech Signals

Simultaneous Codeword Optimization (SimCO) for Dictionary Update and Learning

Learning Ancestral Atom via Sparse Coding

Online Sparsifying Transform Learning—Part II: Convergence Analysis

Correlation Based Online Dictionary Learning Algorithm

Learning Sparse Representations of Depth